Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment
Passenger incidence (station arrival) behavior has been studied primarily to understand how changes to a transit service will affect passenger waiting times. The impact of one intervention (i.e. increasing frequency) could be overestimated compared to another (i.e. improving reliability), depending on the assumption of incidence behavior. It is important to understand passenger incidence so that management decisions will be based on realistic behavioral assumptions. Prior studies on passenger incidence chose their data samples from stations with a single service pattern such that the linking of passengers to services was straightforward. This simplifies the analysis but heavily limits the stations that can be studied. In any moderately complex network, many stations may have more than one service patterns. This limitation prevents it from being systematically applied to the whole network and limits its use in practice.
This paper concerns with incidence behavior in stations with heterogeneous services. It proposes a method to estimate incidence headway and waiting time by integrating disaggregate smartcard data with published timetables using schedule-based assignment. We apply this method to stations in the entire London Overground to demonstrate its practicality and observe that incidence behavior varies across the network and across times of day, reflecting the different headways and reliability. Incidence is much less timetable-dependent on the North London Line than on the other lines because of shorter headways and poorer reliability. Where incidence is timetable-dependent, passengers reduce their mean scheduled waiting time by over 3 minutes compared with random incidence.